Foraging in a changing environment: Simulations in the operant laboratory

Dow, S. M.; Lea, Stephen E. G.

doi:10.1016/0376-6357(84)90064-0

Cited by 20 publications

(21 citation statements)

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“…This interpretation assumes significance in view of work that casts foraging itself as operant behavior (Shettleworth, 1988). There is a tradition of research to support this view (e.g., Abarca & Fantino, 1982;Dall, Cuthill, Cook, & Morphet, 1997;Dow & Lea, 1987;Fantino & Preston, 1988;Ito & Fantino, 1986;McCarthy, Voss, & Davison, 1994;Mellgren & Brown, 1988;Roberts, 1993;Roche, Stubbs, & Glanz, 1996;Williams & Fantino, 1994. These studies principally involve choices made by nonhuman animals.…”

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confidence: 93%

The behavioral ecology of brand choice: How and what do consumers maximize?

Foxall

James

2003

Psychology and Marketing

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Matching theory predicts choices on concurrent variable ratio schedules on which consumers' brand selection occurs will show maximization via exclusive choice of the richest schedule. However, aggregate studies of consumer choice indicate two modes of consumer brand purchase within a product category: either exclusive purchase of one brand or multibrand purchasing. This article uses brand-selection data from individual consumers to determine whether, at this level of analysis, (a) consumers' purchasing patterns show matching, (b) consumers maximize returns, and, if so, (c) what they maximize. Consumer behavior for fast-moving goods exhibits matching, but in the form of multibrand purchasing rather than exclusive choice. Moreover, for substitutes, brand selection is price sensitive, suggesting both melioration and maximization; for nonsubstitutes, choice is not price sensitive but still appears consistent with maximization of price-and nonpricerelated sources of value. ᭧ 2003 Wiley Periodicals, Inc.Although marketing researchers appreciate the significance of branding, they have only a partial understanding of how consumers react to and work with brands in the choices they make from day to day. Aggregate analyses of consumer brand choice show that comparatively few consumers of a product category are 100% loyal to any particular brand.

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confidence: 93%

The behavioral ecology of brand choice: How and what do consumers maximize?

Foxall

James

2003

Psychology and Marketing

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“…INT-R allows the simulation of realistic operant learning performance on a response-byresponse basis, as opposed to cumulative effect (CE) models that can generate data on session-total basis (Davis et al, 1993, Staddon et al, 1994. This model uses a linear learning operator, which has been used successfully in several contexts within the field of animal learning, including foraging (Dow and Lea, 1987) and choice behavior (Davis et al, 1993). A detailed description of this model is presented in Appendix I. INT-R is able to produce quite complex dynamic patterns, including those involved in processes like melioration, discrimination-reversal learning, and regression effects on learning processes (Davis et al, 1993).…”

Section: Experiments 2: In-silico Simulation Of Group Differencesmentioning

confidence: 99%

A Method for Analyzing Strain Differences in Acquisition of IV Cocaine Self-administration in Mice

et al. 2006

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The techniques currently available for studying drug self-administration in animals offer the unique opportunity to carry out micro-analysis of initial episodes of drug use which are extremely difficult to obtain for human subjects. Nonetheless, traditional self-administration techniques do not allow a cost-effective output of large sample sizes needed for genetic analysis. Additionally, the statistical techniques that allow the integration of within-subject temporal data with genetic information are scant. We therefore propose a two-stage method for analyzing strain differences in dynamic phenotypes for a high-throughput version of the self-administration procedure. On a first phenotype-refinement stage, a change-point algorithm (Gallistel et al. (2004) Proc. Natl Acad. Sci. USA 101:13124-13131) was used to separate individual drug self-administration response curves into three distinct components. In a second stage, strains differences in these indexes were assessed. This two-stage approach is illustrated with drug self-administration data and through a computer simulation.

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“…Foraging in animals actually involves a number of complex sub-tasks, of which handling (consuming or gathering) is but one [35][36][37][38][39][40][41][42][43][44]. But primarily it is a problem in finding a resource.…”

Section: Foraging For Resources In An Open Nonstationary Worldmentioning

confidence: 99%

<title>Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing</title>

Mobus

2000

SPIE Proceedings

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Biological inspiration admits to degrees. This paper describes a new neural processing algorithm inspired by a deeper understanding of the workings of real biological synapses. It is shown that a multi-time domain adaptation approach to encoding causal correlation solves the destructive interference problem encountered by more commonly used learning algorithms. It is also shown how this allows an agent to adapt to nonstationary environments in which longer-term changes in the statistical properties occur and are inherently unpredictable, yet not completely lose useful prior knowledge. Finally, it is suggested that the use of causal correlation coupled with value-based learning may provide pragmatic solutions to some other classical problems in machine learning.

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Foraging in a changing environment: Simulations in the operant laboratory

Cited by 20 publications

References 0 publications

The behavioral ecology of brand choice: How and what do consumers maximize?

The behavioral ecology of brand choice: How and what do consumers maximize?

A Method for Analyzing Strain Differences in Acquisition of IV Cocaine Self-administration in Mice

<title>Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing</title>

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